{"id":"https://openalex.org/W2972866770","doi":"https://doi.org/10.1007/978-3-030-32692-0_22","title":"End-to-End Boundary Aware Networks for Medical Image Segmentation","display_name":"End-to-End Boundary Aware Networks for Medical Image Segmentation","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2972866770","doi":"https://doi.org/10.1007/978-3-030-32692-0_22","mag":"2972866770"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-030-32692-0_22","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-030-32692-0_22","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.biorxiv.org/content/biorxiv/early/2019/09/19/770248.full.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028525720","display_name":"Ali Hatamizadeh","orcid":"https://orcid.org/0000-0002-5349-1996"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]},{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Hatamizadeh","raw_affiliation_strings":["Computer Science Department, University of California, Los Angeles, CA, USA","NVIDIA, Santa Clara, CA, USA","[Computer Science Department, University of California, Los Angeles, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]},{"raw_affiliation_string":"NVIDIA, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I4210127875"]},{"raw_affiliation_string":"[Computer Science Department, University of California, Los Angeles, USA]","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034397232","display_name":"Demetri Terzopoulos","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Demetri Terzopoulos","raw_affiliation_strings":["Computer Science Department, University of California, Los Angeles, CA, USA","[Computer Science Department, University of California, Los Angeles, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]},{"raw_affiliation_string":"[Computer Science Department, University of California, Los Angeles, USA]","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002997328","display_name":"Andriy Myronenko","orcid":"https://orcid.org/0000-0001-8713-7031"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andriy Myronenko","raw_affiliation_strings":["NVIDIA, Santa Clara, CA, USA","[NVIDIA, Santa Clara, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NVIDIA, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I4210127875"]},{"raw_affiliation_string":"[NVIDIA, Santa Clara, USA]","institution_ids":["https://openalex.org/I4210127875"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":null,"fwci":0.893,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75696155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"187","last_page":"194"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7723682522773743},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7639891505241394},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.759555459022522},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.687220573425293},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6252756714820862},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.6076645255088806},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.5879319906234741},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.5514730215072632},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5435185432434082},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5149232149124146},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5110458135604858},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.47358232736587524},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.4570262134075165},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.42164018750190735},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10067543387413025}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7723682522773743},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7639891505241394},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.759555459022522},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.687220573425293},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6252756714820862},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.6076645255088806},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.5879319906234741},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.5514730215072632},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5435185432434082},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5149232149124146},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5110458135604858},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.47358232736587524},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.4570262134075165},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.42164018750190735},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10067543387413025},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1007/978-3-030-32692-0_22","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-030-32692-0_22","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},{"id":"doi:10.1101/770248","is_oa":true,"landing_page_url":"https://doi.org/10.1101/770248","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2019/09/19/770248.full.pdf","source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"},{"id":"pmh:oai:arXiv.org:1908.08071","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1908.08071","pdf_url":"https://arxiv.org/pdf/1908.08071","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2972866770","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1908.08071.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1908.08071","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1908.08071","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"},{"id":"doi:10.13140/rg.2.2.28129.86884","is_oa":true,"landing_page_url":"https://doi.org/10.13140/rg.2.2.28129.86884","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.1101/770248","is_oa":true,"landing_page_url":"https://doi.org/10.1101/770248","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2019/09/19/770248.full.pdf","source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2972866770.pdf","grobid_xml":"https://content.openalex.org/works/W2972866770.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W1901129140","https://openalex.org/W2618200950","https://openalex.org/W2751069891","https://openalex.org/W2885139206","https://openalex.org/W2902617128","https://openalex.org/W2947216968","https://openalex.org/W2957077982","https://openalex.org/W2962914239","https://openalex.org/W2963046541","https://openalex.org/W2963307106","https://openalex.org/W2968993623","https://openalex.org/W2978654747","https://openalex.org/W2982631194","https://openalex.org/W2991471181","https://openalex.org/W6600106573"],"related_works":["https://openalex.org/W2974742461","https://openalex.org/W2969377875","https://openalex.org/W3199373853","https://openalex.org/W1985215121","https://openalex.org/W67296747","https://openalex.org/W3038118331","https://openalex.org/W3035485193","https://openalex.org/W3044795341","https://openalex.org/W3131495976","https://openalex.org/W3142251953","https://openalex.org/W2903013553","https://openalex.org/W2530751448","https://openalex.org/W2963919697","https://openalex.org/W2893045060","https://openalex.org/W3026426756","https://openalex.org/W2732931556","https://openalex.org/W2049855663","https://openalex.org/W2952222533","https://openalex.org/W3082619077","https://openalex.org/W2995631364"],"abstract_inverted_index":{"Abstract":[0],"Fully":[1],"convolutional":[2],"neural":[3],"networks":[4,58],"(CNNs)":[5],"have":[6],"proven":[7],"to":[8,61,119],"be":[9],"effective":[10],"at":[11],"representing":[12],"and":[13,75,79],"classifying":[14],"textural":[15],"information,":[16,66],"thus":[17],"transforming":[18],"image":[19,28,32,55,121],"intensity":[20],"into":[21],"output":[22],"class":[23],"masks":[24],"that":[25,103],"achieve":[26],"semantic":[27],"segmentation.":[29,56,122],"In":[30],"medical":[31,54,120],"analysis,":[33],"however,":[34],"expert":[35],"manual":[36],"segmentation":[37,94,109],"often":[38],"relies":[39],"on":[40,88],"the":[41,89,96],"boundaries":[42],"of":[43,46,91],"anatomical":[44],"structures":[45],"interest.":[47],"We":[48,84],"propose":[49],"boundary":[50,65],"aware":[51],"CNNs":[52],"for":[53,63,115],"Our":[57,100],"are":[59,81],"designed":[60],"account":[62],"organ":[64],"both":[67],"by":[68],"providing":[69],"a":[70],"special":[71],"network":[72],"edge":[73],"branch":[74],"edge-aware":[76],"loss":[77],"terms,":[78],"they":[80],"trainable":[82],"end-to-end.":[83],"validate":[85],"their":[86],"effectiveness":[87],"task":[90],"brain":[92],"tumor":[93],"using":[95],"BraTS":[97],"2018":[98],"dataset.":[99],"experiments":[101],"reveal":[102],"our":[104],"approach":[105],"yields":[106],"more":[107,116],"accurate":[108],"results,":[110],"which":[111],"makes":[112],"it":[113],"promising":[114],"extensive":[117],"application":[118]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}